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| Author | Josep M. Gonfaus; Marco Pedersoli; Jordi Gonzalez; Andrea Vedaldi; Xavier Roca | ||||
| Title | Factorized appearances for object detection | Type | Journal Article | ||
| Year | 2015 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 138 | Issue | Pages | 92–101 | |
Keywords  |
Object recognition; Deformable part models; Learning and sharing parts; Discovering discriminative parts | ||||
| Abstract | Deformable object models capture variations in an object’s appearance that can be represented as image deformations. Other effects such as out-of-plane rotations, three-dimensional articulations, and self-occlusions are often captured by considering mixture of deformable models, one per object aspect. A more scalable approach is representing instead the variations at the level of the object parts, applying the concept of a mixture locally. Combining a few part variations can in fact cheaply generate a large number of global appearances.
A limited version of this idea was proposed by Yang and Ramanan [1], for human pose dectection. In this paper we apply it to the task of generic object category detection and extend it in several ways. First, we propose a model for the relationship between part appearances more general than the tree of Yang and Ramanan [1], which is more suitable for generic categories. Second, we treat part locations as well as their appearance as latent variables so that training does not need part annotations but only the object bounding boxes. Third, we modify the weakly-supervised learning of Felzenszwalb et al. and Girshick et al. [2], [3] to handle a significantly more complex latent structure. Our model is evaluated on standard object detection benchmarks and is found to improve over existing approaches, yielding state-of-the-art results for several object categories. |
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | ISE; 600.063; 600.078 | Approved | no | ||
| Call Number | Admin @ si @ GPG2015 | Serial | 2705 | ||
| Permanent link to this record | |||||
| Author | Joost Van de Weijer; Fahad Shahbaz Khan | ||||
| Title | Fusing Color and Shape for Bag-of-Words Based Object Recognition | Type | Conference Article | ||
| Year | 2013 | Publication | 4th Computational Color Imaging Workshop | Abbreviated Journal | |
| Volume | 7786 | Issue | Pages | 25-34 | |
| Keywords |
Object Recognition; color features; bag-of-words; image classification | ||||
| Abstract | In this article we provide an analysis of existing methods for the incorporation of color in bag-of-words based image representations. We propose a list of desired properties on which bases fusing methods can be compared. We discuss existing methods and indicate shortcomings of the two well-known fusing methods, namely early and late fusion. Several recent works have addressed these shortcomings by exploiting top-down information in the bag-of-words pipeline: color attention which is motivated from human vision, and Portmanteau vocabularies which are based on information theoretic compression of product vocabularies. We point out several remaining challenges in cue fusion and provide directions for future research. | ||||
| Address | Chiba; Japan; March 2013 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-36699-4 | Medium | |
| Area | Expedition | Conference | CCIW | ||
| Notes | CIC; 600.048 | Approved | no | ||
| Call Number | Admin @ si @ WeK2013 | Serial | 2283 | ||
| Permanent link to this record | |||||
| Author | Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | Depth of Valleys Accumulation Algorithm for Object Detection | Type | Conference Article | ||
| Year | 2011 | Publication | 14th Congrès Català en Intel·ligencia Artificial | Abbreviated Journal | |
| Volume | 1 | Issue | 1 | Pages | 71-80 |
| Keywords |
Object Recognition, Object Region Identification, Image Analysis, Image Processing | ||||
| Abstract | This work aims at detecting in which regions the objects in the image are by using information about the intensity of valleys, which appear to surround ob- jects in images where the source of light is in the line of direction than the camera. We present our depth of valleys accumulation method, which consists of two stages: first, the definition of the depth of valleys image which combines the output of a ridges and valleys detector with the morphological gradient to measure how deep is a point inside a valley and second, an algorithm that denotes points of the image as interior to objects those which are inside complete or incomplete boundaries in the depth of valleys image. To evaluate the performance of our method we have tested it on several application domains. Our results on object region identification are promising, specially in the field of polyp detection in colonoscopy videos, and we also show its applicability in different areas. | ||||
| Address | Lleida | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-60750-841-0 | Medium | ||
| Area | 800 | Expedition | Conference | CCIA | |
| Notes | MV;SIAI | Approved | no | ||
| Call Number | IAM @ iam @ BSV2011b | Serial | 1699 | ||
| Permanent link to this record | |||||
| Author | Marc Bolaños; Maite Garolera; Petia Radeva | ||||
| Title | Object Discovery using CNN Features in Egocentric Videos | Type | Conference Article | ||
| Year | 2015 | Publication | Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 | Abbreviated Journal | |
| Volume | 9117 | Issue | Pages | 67-74 | |
| Keywords |
Object discovery; Egocentric videos; Lifelogging; CNN | ||||
| Abstract | Lifelogging devices based on photo/video are spreading faster everyday. This growth can represent great benefits to develop methods for extraction of meaningful information about the user wearing the device and his/her environment. In this paper, we propose a semi-supervised strategy for easily discovering objects relevant to the person wearing a first-person camera. The egocentric video sequence acquired by the camera, uses both the appearance extracted by means of a deep convolutional neural network and an object refill methodology that allow to discover objects even in case of small amount of object appearance in the collection of images. We validate our method on a sequence of 1000 egocentric daily images and obtain results with an F-measure of 0.5, 0.17 better than the state of the art approach. | ||||
| Address | Santiago de Compostela; España; June 2015 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-319-19389-2 | Medium | |
| Area | Expedition | Conference | IbPRIA | ||
| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ BGR2015 | Serial | 2596 | ||
| Permanent link to this record | |||||
| Author | Angel Morera; Angel Sanchez; Angel Sappa; Jose F. Velez | ||||
| Title | Robust Detection of Outdoor Urban Advertising Panels in Static Images | Type | Conference Article | ||
| Year | 2019 | Publication | 18th International Conference on Practical Applications of Agents and Multi-Agent Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 246-256 | ||
| Keywords |
Object detection; Urban ads panels; Deep learning; Single Shot Detector (SSD) architecture; Intersection over Union (IoU) metric; Augmented Reality | ||||
| Abstract | One interesting publicity application for Smart City environments is recognizing brand information contained in urban advertising panels. For such a purpose, a previous stage is to accurately detect and locate the position of these panels in images. This work presents an effective solution to this problem using a Single Shot Detector (SSD) based on a deep neural network architecture that minimizes the number of false detections under multiple variable conditions regarding the panels and the scene. Achieved experimental results using the Intersection over Union (IoU) accuracy metric make this proposal applicable in real complex urban images. | ||||
| Address | Aquila; Italia; June 2019 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | PAAMS | ||
| Notes | MSIAU; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ MSS2019 | Serial | 3270 | ||
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| Author | Ivan Huerta; Marco Pedersoli; Jordi Gonzalez; Alberto Sanfeliu | ||||
| Title | Combining where and what in change detection for unsupervised foreground learning in surveillance | Type | Journal Article | ||
| Year | 2015 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 48 | Issue | 3 | Pages | 709-719 |
| Keywords |
Object detection; Unsupervised learning; Motion segmentation; Latent variables; Support vector machine; Multiple appearance models; Video surveillance | ||||
| Abstract | Change detection is the most important task for video surveillance analytics such as foreground and anomaly detection. Current foreground detectors learn models from annotated images since the goal is to generate a robust foreground model able to detect changes in all possible scenarios. Unfortunately, manual labelling is very expensive. Most advanced supervised learning techniques based on generic object detection datasets currently exhibit very poor performance when applied to surveillance datasets because of the unconstrained nature of such environments in terms of types and appearances of objects. In this paper, we take advantage of change detection for training multiple foreground detectors in an unsupervised manner. We use statistical learning techniques which exploit the use of latent parameters for selecting the best foreground model parameters for a given scenario. In essence, the main novelty of our proposed approach is to combine the where (motion segmentation) and what (learning procedure) in change detection in an unsupervised way for improving the specificity and generalization power of foreground detectors at the same time. We propose a framework based on latent support vector machines that, given a noisy initialization based on motion cues, learns the correct position, aspect ratio, and appearance of all moving objects in a particular scene. Specificity is achieved by learning the particular change detections of a given scenario, and generalization is guaranteed since our method can be applied to any possible scene and foreground object, as demonstrated in the experimental results outperforming the state-of-the-art. | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | ISE; 600.063; 600.078 | Approved | no | ||
| Call Number | Admin @ si @ HPG2015 | Serial | 2589 | ||
| Permanent link to this record | |||||
| Author | Arnau Baro; Pau Riba; Alicia Fornes | ||||
| Title | Musigraph: Optical Music Recognition Through Object Detection and Graph Neural Network | Type | Conference Article | ||
| Year | 2022 | Publication | Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition (ICFHR2022) | Abbreviated Journal | |
| Volume | 13639 | Issue | Pages | 171-184 | |
| Keywords |
Object detection; Optical music recognition; Graph neural network | ||||
| Abstract | During the last decades, the performance of optical music recognition has been increasingly improving. However, and despite the 2-dimensional nature of music notation (e.g. notes have rhythm and pitch), most works treat musical scores as a sequence of symbols in one dimension, which make their recognition still a challenge. Thus, in this work we explore the use of graph neural networks for musical score recognition. First, because graphs are suited for n-dimensional representations, and second, because the combination of graphs with deep learning has shown a great performance in similar applications. Our methodology consists of: First, we will detect each isolated/atomic symbols (those that can not be decomposed in more graphical primitives) and the primitives that form a musical symbol. Then, we will build the graph taking as root node the notehead and as leaves those primitives or symbols that modify the note’s rhythm (stem, beam, flag) or pitch (flat, sharp, natural). Finally, the graph is translated into a human-readable character sequence for a final transcription and evaluation. Our method has been tested on more than five thousand measures, showing promising results. | ||||
| Address | December 04 – 07, 2022; Hyderabad, India | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICFHR | ||
| Notes | DAG; 600.162; 600.140; 602.230 | Approved | no | ||
| Call Number | Admin @ si @ BRF2022b | Serial | 3740 | ||
| Permanent link to this record | |||||
| Author | Lorenzo Seidenari; Giuseppe Serra; Andrew Bagdanov; Alberto del Bimbo | ||||
| Title | Local pyramidal descriptors for image recognition | Type | Journal Article | ||
| Year | 2014 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 36 | Issue | 5 | Pages | 1033 - 1040 |
| Keywords |
Object categorization; local features; kernel methods | ||||
| Abstract | In this paper we present a novel method to improve the flexibility of descriptor matching for image recognition by using local multiresolution
pyramids in feature space. We propose that image patches be represented at multiple levels of descriptor detail and that these levels be defined in terms of local spatial pooling resolution. Preserving multiple levels of detail in local descriptors is a way of hedging one’s bets on which levels will most relevant for matching during learning and recognition. We introduce the Pyramid SIFT (P-SIFT) descriptor and show that its use in four state-of-the-art image recognition pipelines improves accuracy and yields state-of-the-art results. Our technique is applicable independently of spatial pyramid matching and we show that spatial pyramids can be combined with local pyramids to obtain further improvement.We achieve state-of-the-art results on Caltech-101 (80.1%) and Caltech-256 (52.6%) when compared to other approaches based on SIFT features over intensity images. Our technique is efficient and is extremely easy to integrate into image recognition pipelines. |
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0162-8828 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP; 600.079 | Approved | no | ||
| Call Number | Admin @ si @ SSB2014 | Serial | 2524 | ||
| Permanent link to this record | |||||
| Author | Idoia Ruiz; Joan Serrat | ||||
| Title | Hierarchical Novelty Detection for Traffic Sign Recognition | Type | Journal Article | ||
| Year | 2022 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 22 | Issue | 12 | Pages | 4389 |
| Keywords |
Novelty detection; hierarchical classification; deep learning; traffic sign recognition; autonomous driving; computer vision | ||||
| Abstract | Recent works have made significant progress in novelty detection, i.e., the problem of detecting samples of novel classes, never seen during training, while classifying those that belong to known classes. However, the only information this task provides about novel samples is that they are unknown. In this work, we leverage hierarchical taxonomies of classes to provide informative outputs for samples of novel classes. We predict their closest class in the taxonomy, i.e., its parent class. We address this problem, known as hierarchical novelty detection, by proposing a novel loss, namely Hierarchical Cosine Loss that is designed to learn class prototypes along with an embedding of discriminative features consistent with the taxonomy. We apply it to traffic sign recognition, where we predict the parent class semantics for new types of traffic signs. Our model beats state-of-the art approaches on two large scale traffic sign benchmarks, Mapillary Traffic Sign Dataset (MTSD) and Tsinghua-Tencent 100K (TT100K), and performs similarly on natural images benchmarks (AWA2, CUB). For TT100K and MTSD, our approach is able to detect novel samples at the correct nodes of the hierarchy with 81% and 36% of accuracy, respectively, at 80% known class accuracy. | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | ADAS; 600.154 | Approved | no | ||
| Call Number | Admin @ si @ RuS2022 | Serial | 3684 | ||
| Permanent link to this record | |||||
| Author | Debora Gil; Aura Hernandez-Sabate; Mireia Brunat;Steven Jansen; Jordi Martinez-Vilalta | ||||
| Title | Structure-preserving smoothing of biomedical images | Type | Journal Article | ||
| Year | 2011 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 44 | Issue | 9 | Pages | 1842-1851 |
| Keywords |
Non-linear smoothing; Differential geometry; Anatomical structures; segmentation; Cardiac magnetic resonance; Computerized tomography | ||||
| Abstract | Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images. | ||||
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM; ADAS | Approved | no | ||
| Call Number | IAM @ iam @ GHB2011 | Serial | 1526 | ||
| Permanent link to this record | |||||
| Author | Debora Gil; Aura Hernandez-Sabate; Mireia Burnat; Steven Jansen; Jordi Martinez-Vilalta | ||||
| Title | Structure-Preserving Smoothing of Biomedical Images | Type | Conference Article | ||
| Year | 2009 | Publication | 13th International Conference on Computer Analysis of Images and Patterns | Abbreviated Journal | |
| Volume | 5702 | Issue | Pages | 427-434 | |
| Keywords |
non-linear smoothing; differential geometry; anatomical structures segmentation; cardiac magnetic resonance; computerized tomography. | ||||
| Abstract | Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images. | ||||
| Address | Münster, Germany | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-03766-5 | Medium | |
| Area | Expedition | Conference | CAIP | ||
| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ GHB2009 | Serial | 1527 | ||
| Permanent link to this record | |||||
| Author | G.Thorvaldsen; Joana Maria Pujadas-Mora; T.Andersen ; L.Eikvil; Josep Llados; Alicia Fornes; Anna Cabre | ||||
| Title | A Tale of two Transcriptions | Type | Journal | ||
| Year | 2015 | Publication | Historical Life Course Studies | Abbreviated Journal | |
| Volume | 2 | Issue | Pages | 1-19 | |
| Keywords |
Nominative Sources; Census; Vital Records; Computer Vision; Optical Character Recognition; Word Spotting | ||||
| Abstract | non-indexed
This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longest series of preserved vital records. Thus, in the Project “Five Centuries of Marriages” (5CofM) at the Autonomous University of Barcelona’s Center for Demographic Studies, the Barcelona Historical Marriage Database has been built. More than 600,000 records were transcribed by 150 transcribers working online. The Norwegian material is cross-sectional as it is the 1891 census, recorded on one sheet per person. This format and the underlining of keywords for several variables made it more feasible to semi-automate data entry than when many persons are listed on the same page. While Optical Character Recognition (OCR) for printed text is scientifically mature, computer vision research is now focused on more difficult problems such as handwriting recognition. In the marriage project, document analysis methods have been proposed to automatically recognize the marriage licenses. Fully automatic recognition is still a challenge, but some promising results have been obtained. In Spain, Norway and elsewhere the source material is available as scanned pictures on the Internet, opening up the possibility for further international cooperation concerning automating the transcription of historic source materials. Like what is being done in projects to digitize printed materials, the optimal solution is likely to be a combination of manual transcription and machine-assisted recognition also for hand-written sources. |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 2352-6343 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.077; 602.006 | Approved | no | ||
| Call Number | Admin @ si @ TPA2015 | Serial | 2582 | ||
| Permanent link to this record | |||||
| Author | Bhalaji Nagarajan; Ricardo Marques; Marcos Mejia; Petia Radeva | ||||
| Title | Class-conditional Importance Weighting for Deep Learning with Noisy Labels | Type | Conference Article | ||
| Year | 2022 | Publication | 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | Abbreviated Journal | |
| Volume | 5 | Issue | Pages | 679-686 | |
| Keywords |
Noisy Labeling; Loss Correction; Class-conditional Importance Weighting; Learning with Noisy Labels | ||||
| Abstract | Large-scale accurate labels are very important to the Deep Neural Networks to train them and assure high performance. However, it is very expensive to create a clean dataset since usually it relies on human interaction. To this purpose, the labelling process is made cheap with a trade-off of having noisy labels. Learning with Noisy Labels is an active area of research being at the same time very challenging. The recent advances in Self-supervised learning and robust loss functions have helped in advancing noisy label research. In this paper, we propose a loss correction method that relies on dynamic weights computed based on the model training. We extend the existing Contrast to Divide algorithm coupled with DivideMix using a new class-conditional weighted scheme. We validate the method using the standard noise experiments and achieved encouraging results. | ||||
| Address | Virtual; February 2022 | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ NMM2022 | Serial | 3798 | ||
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| Author | Asma Bensalah; Alicia Fornes; Cristina Carmona_Duarte; Josep Llados | ||||
| Title | Easing Automatic Neurorehabilitation via Classification and Smoothness Analysis | Type | Conference Article | ||
| Year | 2022 | Publication | Intertwining Graphonomics with Human Movements. 20th International Conference of the International Graphonomics Society, IGS 2022 | Abbreviated Journal | |
| Volume | 13424 | Issue | Pages | 336-348 | |
| Keywords |
Neurorehabilitation; Upper-lim; Movement classification; Movement smoothness; Deep learning; Jerk | ||||
| Abstract | Assessing the quality of movements for post-stroke patients during the rehabilitation phase is vital given that there is no standard stroke rehabilitation plan for all the patients. In fact, it depends basically on the patient’s functional independence and its progress along the rehabilitation sessions. To tackle this challenge and make neurorehabilitation more agile, we propose an automatic assessment pipeline that starts by recognising patients’ movements by means of a shallow deep learning architecture, then measuring the movement quality using jerk measure and related measures. A particularity of this work is that the dataset used is clinically relevant, since it represents movements inspired from Fugl-Meyer a well common upper-limb clinical stroke assessment scale for stroke patients. We show that it is possible to detect the contrast between healthy and patients movements in terms of smoothness, besides achieving conclusions about the patients’ progress during the rehabilitation sessions that correspond to the clinicians’ findings about each case. | ||||
| Address | June 7-9, 2022, Las Palmas de Gran Canaria, Spain | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | IGS | ||
| Notes | DAG; 600.121; 600.162; 602.230; 600.140 | Approved | no | ||
| Call Number | Admin @ si @ BFC2022 | Serial | 3738 | ||
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| Author | C. Gratin; Jordi Vitria; F. Moreso; D. Seron | ||||
| Title | Texture Classification using Neural Networks and Local Granulometries | Type | Conference Article | ||
| Year | 1994 | Publication | EURASIP Workshop, Mathematical Morphology and Its Applications to image Processing, J.Serra and P.Soille, editors | Abbreviated Journal | |
| Volume | Issue | Pages | 309-316 | ||
| Keywords |
Neural Networks; Granulometry; Kidney; Texture; Classication | ||||
| Abstract | |||||
| Address | Fointanebleau, France | ||||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ GVM1994 | Serial | 110 | ||
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